Method for quantification of purity of sub-visible particle samples

ABSTRACT

The method is for quantification of purity of sub-visible particle samples. A sample to be analyzed is place in an electron microscope to obtain an electron microscopy image of the sample. The sample contains objects. The objects that have sizes being different from a size range of primary particles and sizes being within the size range of primary particles are enhanced. The objects are detected as being primary particles or debris. The detected primary particles are excluded from the objects so that the objects contain debris but no primary particles. A first total area (T1) of the detected debris is measured. A second total area (T2) of the detected primary particles is measured.

PRIOR APPLICATION

This application is a U.S. national phase application based onInternational Application No. PCT/US17/50962, filed 11 Sep. 2017,claiming priority from U.S. Provisional Patent Application No.62/402,003, filed 30 Sep. 2016.

TECHNICAL FIELD

The present invention relates to a method for assessing andquantitatively measuring how pure a sample is by using electronmicroscopy.

BACKGROUND AND SUMMARY OF THE INVENTION

Developing and producing biopharmaceuticals typically involve severalpurification steps where cell debris, broken particles, othercontaminants and clusters etc. should be removed so that the finalproduct contains only the desirable primary particles. The purity anddispersion of the primary particles of interest (i.e. non-clusteredprimary particles) in the final product is important for its quality andefficacy. To quantitatively assess the purity is hence of importance forthe final product but also during the upstream development andproduction processes to evaluate the efficacy and effect of eachpurification step. Electron microscopy is a method by which sub-visibleparticles can be imaged at a resolution sufficient to identifying theparticles of interest (primary particles) as well as undesirable debris,contaminants and clusters in the sample. An objective quantitativemeasure of how pure a sample of sub-visible particles such as virusparticles, virus-like particles, inorganic beads and other nanoparticlesand micro-particles from liquid samples is important in many processes.For example, modified virus vectors are commonly used in gene therapyapplications and modified virus particles are used as vaccines. However,the currently available methods for quantitatively assessing the purityare not very accurate and often involve manual steps that may distortthe final result. There is a need for a more effective and reliablemethod to assess and measure the purity of liquid samples that containsub-visible primary particles and contaminants/debris.

The method of the present invention provides a solution to theabove-outlined problems. More particularly, the method is forquantification of purity of sub-visible particle samples. A sample to beanalyzed is placed in an electron microscope to obtain an electronmicroscopy image of the sample. The sample contains objects of primaryparticles as well as debris. Debris could be broken or parts (sub-units)of primary particles, and/or contaminants, and/or primary particle ordebris clusters or aggregates, and or left-over material from theproduction phase. The objects in the image are enhanced and have sizesthat are different from a size range of primary particles and sizes thatare within the size range of primary particles. The objects in the imageare detected as being primary particles or debris. The detected primaryparticles are excluded from the remaining objects so that the objectsdetected as debris contain only debris and no primary particles. A firsttotal area (T1) of the detected debris is measured. A second total area(T2) of the detected primary particles is measured. A ratio of the firsttotal area (T1) to the second total area (T2) is calculated to determinea quantitative measurement of purity of the sample.

In another embodiment, the edges of objects in the image are enhancedand the objects have a size that is substantially similar to a sizerange of primary particles. A roundness of the objects is analyzed toidentify primary particles.

In another embodiment, objects in the image that have a shape that issubstantially similar to that of primary particles are identified asprimary particles.

In another embodiment, the edges of objects in the image are enhancedand the objects have a size that is substantially similar to a sizerange of primary particles and a radial density profile of the objectsis analyzed to identify primary particles.

In yet another embodiment, the edges of objects in the image areenhanced and the objects have a size that is substantially similar to asize range of primary particles, and a signal-to-noise ratio at theborder of the objects are analyzed by measuring an average intensity ofan interior of the objects compared to an average intensity just outsidethe objects.

In another embodiment, the edges of objects in the image are enhancedand the objects have a size that is substantially similar to a sizerange of primary particles and a local contrast of the objects aremeasured by analyzing a sharpness of an outer edge of the objects.

In another embodiment, the edges of objects in the image are enhancedand the objects have a size that is substantially similar to a sizerange of primary particles and the structure of the objects is measuredby means of texture analysis to identify primary particles.

In another embodiment, the structure of the objects in the image ismeasured by means of texture analysis and analyzed to identify primaryparticles.

In another embodiment, a sample that contains virus particles orvirus-like particles is placed in the electron microscope.

In yet another embodiment, the image is filtered with two smoothingfilters to create a first filtered image and a second filtered image andsubtracting the first filtered image from the second filtered image.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a transmission electron image of a negatively stainedbiological particle sample in solution;

FIG. 2 is an image showing the result after the difference of Gaussiansmethod has been used to enhance fine edges;

FIG. 3 is an image of primary objects detected in the image shown inFIG. 2 by using a specific detection method;

FIG. 4 is an image showing the result after enhancing objects of typicaldebris size using the difference of Gaussians method on the originalimage;

FIG. 5 is an image showing the result after thresholding theobject-enhanced image;

FIG. 6 is an image showing the result after removing objectscorresponding to the primary particles; and

FIG. 7 is an image showing the final result including both primaryparticles and debris.

DETAILED DESCRIPTION

The present invention describes a unique method for quantitativelymeasuring the purity of a sample containing sub-visible ornano-particles (that may, for example, have a size of about 100 nm) insolution based on an automatic and objective image analysis of electronmicroscopy images of the sample. The sample may, for example, be liquid,dissolved solid or powder samples.

Negative stain transmission electron microscopy images may be used. Ingeneral, the purity measure of the present invention is, preferably, thearea ratio of primary particles to non-primary particles (includingsmall debris as well as large debris clusters). The principle steps ofthe method of the present invention are:

-   -   1. Placing a sample to be analyzed in an electron microscope to        obtain an electron microscopy image of the sample;    -   2. Enhancing edges (such as fine edges) of primary particles in        the image that have a size that is typical for the primary        particle;    -   3. Specifically detecting all primary particles in the image by        using a method that is adapted to identify the particular        primary particles;    -   4. Enhancing objects of sizes typical for debris clusters and        contaminants in the image;    -   5. Detecting all the enhanced objects in step 4 by using e.g., a        thresholding method.    -   6. Excluding (subtracting) the identified primary particles from        the detected enhanced objects;    -   7. Measuring a total area of the detected and remaining debris        clusters and contaminants from step 6;    -   8. Measuring a total area of the detected primary particles        detected in step 3; and    -   9. Calculating a ratio of the area resulting from step 7 to the        area resulting from step 8.

A typical example image 100 is shown in FIG. 1, and steps 2-6 areillustrated in FIGS. 2-6. FIG. 7 shows the final result 102, i.e., theprimary particles 120 and debris objects 106. The measured areas of theprimary particles and debris objects, respectively, are used in steps7-9 to derive the purity measure.

More particularly, FIG. 1 is a transmission electron microscopy image100 of a negatively stained biological particle sample in solution. Thebiological particles may be virus particles or any other organicparticles. Samples that contain inorganic particles may also be analyzedwith the method of the present invention. A suitable method such as thedifference of Gaussians or any other suitable method may be used toenhance, for example, the fine edges (or contrast/thin regions) of theobjects 116 in the image that have a size that is typical for theprimary particle (step 2) to be analyzed. FIG. 2 shows the result 105after the difference of Gaussians method has been used to enhance thefine edges of the identified objects 116. The identified object 116 aremostly primary particles but may contain some undesirable debris andcontaminants 106 (explained in more detail below). A certain type ofvirus particles may have an expected size of 100 nm so that particlesthat have a size that is substantially different are most likely notprimary particles but undesirable debris particles 106 instead. Itshould be noted that the analysis in step 2 may not be limited to thesize of the entire particles. It may also be possible to focus on partsof the structure of the primary particle that are specific to thedesired primary particles such as the pattern of the particle orthickness of the outer edge. It is then possible to enhance a specificportion of the particle such as the pattern or the thickness of theouter edge of the particle.

The enhancement of fine edges of selected particles or primary particles(such as by using the difference of Gaussian) in the image has providedunexpected and surprisingly good results. For example, stainingbiological samples (such as virus particles) results in differentamounts of stain surrounding the objects/particles in the image due todifferent thickness of the stain in different parts of the sample aswell as around differently sized objects/particles. The amount(thickness) of the stain and hence where on the grid the purity measureis calculated directly influences the purity measure and may make themeasurement less correct. As indicated above, edges or objects in aselected size range in the image are enhanced by using, for example, thedifference of Gaussian approach. It is to be understood that other edgeor object enhancing approaches may also be used. In the difference ofGaussians method, an image is filtered with two Gaussian smoothingfilters (that have different smoothing factor sigma). One filtered imageis then subtracted from the other which results in an image withenhanced edges or enhanced objects of, for example, a certain size. Theresult partly depends on the combination of smoothing factors used. Itis on these modified images that the primary particles, debris andcontaminants are then detected. As indicated above, the edge/objectenhancing step reduces the effect of different amounts and unevendistribution of the stain. This important and innovative step is hencenecessary in application/cases where uneven stain is a problem such aswhen analyzing biological samples of virus particles and otherparticles. In samples that only contain inorganic material/particles,the edge enhancing step may be excluded. In addition, enhancing objects(i.e. primary particles in steps 2/3) of a certain selected size orcharacteristic, such as edge thickness or pattern, prior to detectingall objects after enhancement according to step 4 (described below),reduces the problem of uneven background coloring and lighting in theimage which otherwise could easily lead to false positioning of theobject borders and even result in falsely or incorrectly missed ordetected objects. In other words, the enhancement in step 2 makes iteasier to identify the primary particles in view of the variedbackground colors and lighting from the microscope. For example, theenhancement removes or reduces the effect of lighter colors in certainsegments of the image and the effect of gradients of intensity.

However, some of the undesirable debris and contamination particles mayhave a size that is similar to that of the primary particles to beanalyzed. In other words, the enhancement in step 2 may enhanceparticles that happen to have a similar size or have othercharacteristics similar to primary particles but they are not primaryparticles. It is then necessary to further analyze the enhanced objectsin step 2 by, for example, analyzing the shape or roundness of theobjects 116 in order to identify and distinguish primary particles 120from debris that may have sizes that are within the size range of theprimary particles. This is done in step 3 (and the result 107 is shownin FIG. 3) that identifies (in white) the detected primary particles 120by using a detection method such as analyzing the radial symmetry of theparticles/objects identified in step 2 and as shown in FIG. 2.

In order to make the method of the present invention and thequantitative purity measure objective (i.e. user unbiased) and robust,the steps of the present method should preferably be performedautomatically with user input only provided to select the approximatesizes of the primary particles as well as lower and upper limits of thenon-primary particles/objects (debris and clusters). An important aspectof the present invention is the ability to automatically distinguish theprimary particles from non-primary particles/objects. As indicatedregarding step 3, circular primary particles (such as viral vectors)can, for example, be detected based on the circular symmetriccharacteristics or other methods that specifically detect the primaryparticles 120. For example, the radially symmetric virus particlestructure may be transformed to a gray-level profile. It may be used todescribe the structure by calculating the mean gray-level at eachdistance from the center going from the center and out towards aperiphery or shell of the virus particle structure. It is also possibleto develop mathematical algorithms to describe the virus particlestructures or shape instead of relying on gray scale profiles.

An important feature of the method of the present invention is that itis possible to create templates based on the gray scale profiles toobjectively describe the virus particles. The templates may be createdby using mathematical methods also. In this way, all the detectedobjects in a size range may be compared to a profile or template thatrepresent a typical primary particle and use the profile/template todetermine if the detected object is sufficiently similar to theprofile/template to be classified as a true primary particle 120. It mayalso be possible to use other methods in step 3 to identify the primaryparticles 120 such as methods to detect elliptical, rod-like orcrystal-like shapes.

It is to be understood that the above reference to virus particles ismerely an example and the present invention is not limited to virusparticles. Also, the reference to the circularity/roundness of theparticle is merely an example, and other characteristics such asspecific patterns, specific shapes and object surfaces may also be used.

Regarding step 3 that is related to the specific detection of primaryparticles 120 (and to eliminate non-primary particles that have a sizethat is similar to primary particles), an additional step may beincluded, that uses the signal-to-noise ratio or local contrasts at theborder of the objects 116. This is done to further improve the detectionof primary particles 120 and the automatic decision about what is aprimary particle or not. The signal-to-noise ratio is, preferably,measured as the average intensity in the interior of the particlecompared to the average intensity just outside the particle. The localcontrast approach may be used to analyze how sharp the outer and/orinner edges of the particle are to better be able to determine whetherit is a primary particle or not. Also regarding step 3, fornon-spherical particles, other methods designed for the detection ofspecific shapes or other characteristics of the primary particles 120,such as texture (pattern on the particle surface), can be used.

The next step is to detect undesirable debris and contaminants in thesample. FIG. 4 shows the result 108 after objects have been enhanced onthe original image. One problem is that the analysis and enhancement instep 4 also identifies and includes some or all of the primary particles120 identified in step 3. In step 4, all objects 114 in the image areenhanced by using, for example, the difference of Gaussians method.Debris and clusters can, for example, also be detected by an (automatic)intensity thresholding method such as Otsu's thresholding method.Manually choosing the intensity threshold would also work but it couldeasily introduce undesired user-bias.

Preferably, the objects 114 are identified by focusing on a certain sizerange that is typical for debris cluster and contaminants becausedebris/contaminants may have any shape and colors. It is possible tofirst focus on objects with sizes that are typical for debris andcontaminants but smaller than primary particles and then focus onobjects with sizes that are larger than primary particles. The intensityof the objects 114 is, preferably, analyzed to identify regions or areasthat include debris and contaminants.

In this way, all the enhanced objects 114 in the image are detected instep 5 and the result 110 is shown in FIG. 5. In other words, theidentification method used in step 4 is not sufficiently specific toexclude the primary particles 120 so that both debris particles and someor all of the primary particles are identified as objects 114 and shownin result 110.

In step 6, the identified primary particles 120, as identified in step3, that are also included in objects 114 are excluded or subtracted fromthe objects 114 shown in FIG. 5 so that only the detected debris andcontaminants 106 are shown in result 112 in FIG. 6. This means anyprimary particles, that happened to have been included in objects 114 asa result of the enhancement method used in step 5, are removed so thatresult 112 only shows debris and contamination particles 106.

In step 7, the total area T1 of the remaining objects or debris 106after step 6, i.e. after the primary particles have been removed, ismeasured. In step 8, the total area T2 of the detected primary particles120, as shown in FIG. 3, is measured. In step 9, the ratio R of theareas resulting after step 7 and step 8, respectively, is calculated.

The described approach, quantifying purity as the area ratio of primaryparticles vs. other objects is robust. A few falsely detected or missedprimary particles only affect the result in a nonsignificant way sincethe measurement is based on a large number of images that represent thesample well, preferably resulting from automated image acquisition (userunbiased) or manually acquired images.

While the present invention has been described in accordance withpreferred compositions and embodiments, it is to be understood thatcertain substitutions and alterations may be made thereto withoutdeparting from the spirit and scope of the following claims.

We claim:
 1. A method for quantification of purity of sub-visibleparticle samples, comprising: placing a sample to be analyzed in anelectron microscope to obtain an electron microscopy image of thesample, the sample containing objects; enhancing the objects in theimage having sizes being different from a size range of primaryparticles and sizes being within the size range of primary particles;detecting the objects in the image as being primary particles or debris;excluding the detected primary particles from the objects so thatobjects contains debris but no primary particles; measuring a firsttotal area (T1) of the detected debris; measuring a second total area(T2) of the detected primary particles; and calculating a ratio of thefirst total area (T1) to the second total area (T2) to determine aquantitative measurement of purity of the sample.
 2. The methodaccording to claim 1 wherein the method further comprises enhancingedges of objects in the image that have a size that is substantiallysimilar to a size range of primary particles and analyzing a roundnessof the objects to identify primary particles.
 3. The method according toclaim 1 wherein the method further comprises enhancing edges of objectsin the image that have a size that is substantially similar to a sizerange of primary particles and analyzing a radial density profile of theobjects to identify primary particles.
 4. The method according to claim1 wherein the method further comprises enhancing edges of objects in theimage that have a size that is substantially similar to a size range ofprimary particles and analyzing a signal-to-noise ratio at the border ofthe objects by measuring an average intensity of an interior of theobjects compared to an average intensity just outside the objects. 5.The method according to claim 1 wherein the method further comprisesenhancing edges of objects in the image that have a size that issubstantially similar to a size range of primary particles and analyzinga local contrast of the objects by analyzing a sharpness of an edge ofthe objects.
 6. The method according to claim 1 wherein the methodfurther comprises placing a sample that contains virus or virus-likeparticles in the electron microscope.
 7. The method according to claim 1wherein the method further comprises filtering the image with twosmoothing filters to create a first filtered image and a second filteredimage and subtracting the first filtered image from the second filteredimage.